Dataset statistics
| Number of variables | 18 |
|---|---|
| Number of observations | 1000 |
| Missing cells | 317 |
| Missing cells (%) | 1.8% |
| Duplicate rows | 0 |
| Duplicate rows (%) | 0.0% |
| Total size in memory | 140.8 KiB |
| Average record size in memory | 144.1 B |
Variable types
| Categorical | 9 |
|---|---|
| Numeric | 6 |
| DateTime | 1 |
| Boolean | 2 |
deceased_indicator has constant value "False" | Constant |
country has constant value "Australia" | Constant |
first_name has a high cardinality: 940 distinct values | High cardinality |
last_name has a high cardinality: 961 distinct values | High cardinality |
job_title has a high cardinality: 184 distinct values | High cardinality |
address has a high cardinality: 1000 distinct values | High cardinality |
postcode is highly correlated with property_valuation | High correlation |
property_valuation is highly correlated with postcode | High correlation |
Rank is highly correlated with Value | High correlation |
Value is highly correlated with Rank | High correlation |
Rank is highly correlated with Value | High correlation |
Value is highly correlated with Rank | High correlation |
Rank is highly correlated with Value | High correlation |
Value is highly correlated with Rank | High correlation |
gender is highly correlated with deceased_indicator and 1 other fields | High correlation |
job_industry_category is highly correlated with deceased_indicator and 1 other fields | High correlation |
owns_car is highly correlated with deceased_indicator and 1 other fields | High correlation |
state is highly correlated with deceased_indicator and 1 other fields | High correlation |
wealth_segment is highly correlated with deceased_indicator and 1 other fields | High correlation |
deceased_indicator is highly correlated with gender and 5 other fields | High correlation |
country is highly correlated with gender and 5 other fields | High correlation |
gender is highly correlated with job_industry_category | High correlation |
job_industry_category is highly correlated with gender | High correlation |
postcode is highly correlated with state and 1 other fields | High correlation |
state is highly correlated with postcode | High correlation |
property_valuation is highly correlated with postcode | High correlation |
Rank is highly correlated with Value | High correlation |
Value is highly correlated with Rank | High correlation |
last_name has 29 (2.9%) missing values | Missing |
DOB has 17 (1.7%) missing values | Missing |
job_title has 106 (10.6%) missing values | Missing |
job_industry_category has 165 (16.5%) missing values | Missing |
first_name is uniformly distributed | Uniform |
last_name is uniformly distributed | Uniform |
address is uniformly distributed | Uniform |
address has unique values | Unique |
Reproduction
| Analysis started | 2022-03-21 20:50:13.551679 |
|---|---|
| Analysis finished | 2022-03-21 20:50:31.889736 |
| Duration | 18.34 seconds |
| Software version | pandas-profiling v3.1.0 |
| Download configuration | config.json |
| Distinct | 940 |
|---|---|
| Distinct (%) | 94.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 7.9 KiB |
| Rozamond | 3 |
|---|---|
| Mandie | 3 |
| Dorian | 3 |
| Muffin | 2 |
| Tessa | 2 |
| Other values (935) |
Length
| Max length | 13 |
|---|---|
| Median length | 6 |
| Mean length | 6.087 |
| Min length | 2 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 883 ? |
|---|---|
| Unique (%) | 88.3% |
Sample
| 1st row | Chickie |
|---|---|
| 2nd row | Morly |
| 3rd row | Ardelis |
| 4th row | Lucine |
| 5th row | Melinda |
Common Values
| Value | Count | Frequency (%) |
| Rozamond | 3 | 0.3% |
| Mandie | 3 | 0.3% |
| Dorian | 3 | 0.3% |
| Muffin | 2 | 0.2% |
| Tessa | 2 | 0.2% |
| Suzy | 2 | 0.2% |
| Farlie | 2 | 0.2% |
| Kippar | 2 | 0.2% |
| Maddalena | 2 | 0.2% |
| Nobe | 2 | 0.2% |
| Other values (930) | 977 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| rozamond | 3 | 0.3% |
| dorian | 3 | 0.3% |
| mandie | 3 | 0.3% |
| muffin | 2 | 0.2% |
| latrena | 2 | 0.2% |
| shane | 2 | 0.2% |
| anthony | 2 | 0.2% |
| barth | 2 | 0.2% |
| cami | 2 | 0.2% |
| aloysius | 2 | 0.2% |
| Other values (930) | 977 |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
| Distinct | 961 |
|---|---|
| Distinct (%) | 99.0% |
| Missing | 29 |
| Missing (%) | 2.9% |
| Memory size | 7.9 KiB |
| Borsi | 2 |
|---|---|
| Sissel | 2 |
| Shoesmith | 2 |
| Burgoine | 2 |
| Hallt | 2 |
| Other values (956) |
Length
| Max length | 21 |
|---|---|
| Median length | 7 |
| Mean length | 7.026776519 |
| Min length | 3 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 951 ? |
|---|---|
| Unique (%) | 97.9% |
Sample
| 1st row | Brister |
|---|---|
| 2nd row | Genery |
| 3rd row | Forrester |
| 4th row | Stutt |
| 5th row | Hadlee |
Common Values
| Value | Count | Frequency (%) |
| Borsi | 2 | 0.2% |
| Sissel | 2 | 0.2% |
| Shoesmith | 2 | 0.2% |
| Burgoine | 2 | 0.2% |
| Hallt | 2 | 0.2% |
| Minshall | 2 | 0.2% |
| Eade | 2 | 0.2% |
| Van den Velde | 2 | 0.2% |
| Sturch | 2 | 0.2% |
| Crellim | 2 | 0.2% |
| Other values (951) | 951 | |
| (Missing) | 29 | 2.9% |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| van | 3 | 0.3% |
| de | 3 | 0.3% |
| den | 3 | 0.3% |
| borsi | 2 | 0.2% |
| sissel | 2 | 0.2% |
| crellim | 2 | 0.2% |
| sturch | 2 | 0.2% |
| velde | 2 | 0.2% |
| eade | 2 | 0.2% |
| minshall | 2 | 0.2% |
| Other values (960) | 963 |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
| Distinct | 3 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 7.9 KiB |
| Female | |
|---|---|
| Male | |
| U | 17 |
Length
| Max length | 6 |
|---|---|
| Median length | 6 |
| Mean length | 4.975 |
| Min length | 1 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | Male |
|---|---|
| 2nd row | Male |
| 3rd row | Female |
| 4th row | Female |
| 5th row | Female |
Common Values
| Value | Count | Frequency (%) |
| Female | 513 | |
| Male | 470 | |
| U | 17 | 1.7% |
Length
Histogram of lengths of the category
Pie chart
| Value | Count | Frequency (%) |
| female | 513 | |
| male | 470 | |
| u | 17 | 1.7% |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
past_3_years_bike_related_purchases
Real number (ℝ≥0)
| Distinct | 100 |
|---|---|
| Distinct (%) | 10.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 49.836 |
| Minimum | 0 |
|---|---|
| Maximum | 99 |
| Zeros | 9 |
| Zeros (%) | 0.9% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 7.9 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 5 |
| Q1 | 26.75 |
| median | 51 |
| Q3 | 72 |
| 95-th percentile | 94 |
| Maximum | 99 |
| Range | 99 |
| Interquartile range (IQR) | 45.25 |
Descriptive statistics
| Standard deviation | 27.79668613 |
|---|---|
| Coefficient of variation (CV) | 0.5577631858 |
| Kurtosis | -1.088048884 |
| Mean | 49.836 |
| Median Absolute Deviation (MAD) | 22.5 |
| Skewness | -0.06562186172 |
| Sum | 49836 |
| Variance | 772.6557598 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 60 | 20 | 2.0% |
| 59 | 18 | 1.8% |
| 42 | 17 | 1.7% |
| 70 | 17 | 1.7% |
| 11 | 16 | 1.6% |
| 37 | 16 | 1.6% |
| 47 | 15 | 1.5% |
| 84 | 14 | 1.4% |
| 67 | 14 | 1.4% |
| 57 | 14 | 1.4% |
| Other values (90) | 839 |
| Value | Count | Frequency (%) |
| 0 | 9 | |
| 1 | 8 | |
| 2 | 9 | |
| 3 | 9 | |
| 4 | 10 | |
| 5 | 13 | |
| 6 | 10 | |
| 7 | 13 | |
| 8 | 7 | |
| 9 | 5 | 0.5% |
| Value | Count | Frequency (%) |
| 99 | 9 | |
| 98 | 6 | |
| 97 | 11 | |
| 96 | 9 | |
| 95 | 8 | |
| 94 | 12 | |
| 93 | 9 | |
| 92 | 5 | |
| 91 | 8 | |
| 90 | 6 |
| Distinct | 958 |
|---|---|
| Distinct (%) | 97.5% |
| Missing | 17 |
| Missing (%) | 1.7% |
| Memory size | 7.9 KiB |
| Minimum | 1938-06-08 00:00:00 |
|---|---|
| Maximum | 2002-02-27 00:00:00 |
Histogram with fixed size bins (bins=50)
| Distinct | 184 |
|---|---|
| Distinct (%) | 20.6% |
| Missing | 106 |
| Missing (%) | 10.6% |
| Memory size | 7.9 KiB |
| Associate Professor | 15 |
|---|---|
| Environmental Tech | 14 |
| Software Consultant | 14 |
| Chief Design Engineer | 13 |
| Cost Accountant | 12 |
| Other values (179) |
Length
| Max length | 36 |
|---|---|
| Median length | 18 |
| Mean length | 18.08836689 |
| Min length | 5 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 45 ? |
|---|---|
| Unique (%) | 5.0% |
Sample
| 1st row | General Manager |
|---|---|
| 2nd row | Structural Engineer |
| 3rd row | Senior Cost Accountant |
| 4th row | Account Representative III |
| 5th row | Financial Analyst |
Common Values
| Value | Count | Frequency (%) |
| Associate Professor | 15 | 1.5% |
| Environmental Tech | 14 | 1.4% |
| Software Consultant | 14 | 1.4% |
| Chief Design Engineer | 13 | 1.3% |
| Cost Accountant | 12 | 1.2% |
| VP Sales | 12 | 1.2% |
| Assistant Manager | 12 | 1.2% |
| Assistant Media Planner | 12 | 1.2% |
| Senior Sales Associate | 12 | 1.2% |
| VP Quality Control | 11 | 1.1% |
| Other values (174) | 767 | |
| (Missing) | 106 | 10.6% |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| engineer | 131 | 6.3% |
| assistant | 82 | 3.9% |
| manager | 76 | 3.7% |
| analyst | 66 | 3.2% |
| iv | 52 | 2.5% |
| iii | 50 | 2.4% |
| vp | 46 | 2.2% |
| ii | 44 | 2.1% |
| senior | 44 | 2.1% |
| sales | 44 | 2.1% |
| Other values (117) | 1444 |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
| Distinct | 9 |
|---|---|
| Distinct (%) | 1.1% |
| Missing | 165 |
| Missing (%) | 16.5% |
| Memory size | 7.9 KiB |
| Financial Services | |
|---|---|
| Manufacturing | |
| Health | |
| Retail | |
| Property | |
| Other values (4) |
Length
| Max length | 18 |
|---|---|
| Median length | 13 |
| Mean length | 11.31976048 |
| Min length | 2 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | Manufacturing |
|---|---|
| 2nd row | Property |
| 3rd row | Financial Services |
| 4th row | Manufacturing |
| 5th row | Financial Services |
Common Values
| Value | Count | Frequency (%) |
| Financial Services | 203 | |
| Manufacturing | 199 | |
| Health | 152 | |
| Retail | 78 | 7.8% |
| Property | 64 | 6.4% |
| IT | 51 | 5.1% |
| Entertainment | 37 | 3.7% |
| Argiculture | 26 | 2.6% |
| Telecommunications | 25 | 2.5% |
| (Missing) | 165 |
Length
Histogram of lengths of the category
Pie chart
| Value | Count | Frequency (%) |
| financial | 203 | |
| services | 203 | |
| manufacturing | 199 | |
| health | 152 | |
| retail | 78 | 7.5% |
| property | 64 | 6.2% |
| it | 51 | 4.9% |
| entertainment | 37 | 3.6% |
| argiculture | 26 | 2.5% |
| telecommunications | 25 | 2.4% |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
| Distinct | 3 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 7.9 KiB |
| Mass Customer | |
|---|---|
| High Net Worth | |
| Affluent Customer |
Length
| Max length | 17 |
|---|---|
| Median length | 13 |
| Mean length | 14.215 |
| Min length | 13 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | Mass Customer |
|---|---|
| 2nd row | Mass Customer |
| 3rd row | Affluent Customer |
| 4th row | Affluent Customer |
| 5th row | Affluent Customer |
Common Values
| Value | Count | Frequency (%) |
| Mass Customer | 508 | |
| High Net Worth | 251 | |
| Affluent Customer | 241 |
Length
Histogram of lengths of the category
Pie chart
| Value | Count | Frequency (%) |
| customer | 749 | |
| mass | 508 | |
| high | 251 | 11.2% |
| net | 251 | 11.2% |
| worth | 251 | 11.2% |
| affluent | 241 | 10.7% |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
| Distinct | 1 |
|---|---|
| Distinct (%) | 0.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 1.1 KiB |
| False |
|---|
| Value | Count | Frequency (%) |
| False | 1000 |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 1.1 KiB |
| False | |
|---|---|
| True |
| Value | Count | Frequency (%) |
| False | 507 | |
| True | 493 |
tenure
Real number (ℝ≥0)
| Distinct | 23 |
|---|---|
| Distinct (%) | 2.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 11.388 |
| Minimum | 0 |
|---|---|
| Maximum | 22 |
| Zeros | 2 |
| Zeros (%) | 0.2% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 7.9 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 3 |
| Q1 | 7 |
| median | 11 |
| Q3 | 15 |
| 95-th percentile | 20 |
| Maximum | 22 |
| Range | 22 |
| Interquartile range (IQR) | 8 |
Descriptive statistics
| Standard deviation | 5.037144908 |
|---|---|
| Coefficient of variation (CV) | 0.442320417 |
| Kurtosis | -0.8128152156 |
| Mean | 11.388 |
| Median Absolute Deviation (MAD) | 4 |
| Skewness | 0.07089079797 |
| Sum | 11388 |
| Variance | 25.37282883 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=23)
| Value | Count | Frequency (%) |
| 9 | 79 | 7.9% |
| 13 | 74 | 7.4% |
| 11 | 68 | 6.8% |
| 10 | 63 | 6.3% |
| 12 | 61 | 6.1% |
| 5 | 60 | 6.0% |
| 7 | 60 | 6.0% |
| 17 | 59 | 5.9% |
| 15 | 58 | 5.8% |
| 8 | 55 | 5.5% |
| Other values (13) | 363 |
| Value | Count | Frequency (%) |
| 0 | 2 | 0.2% |
| 1 | 8 | 0.8% |
| 2 | 15 | 1.5% |
| 3 | 26 | 2.6% |
| 4 | 36 | |
| 5 | 60 | |
| 6 | 45 | |
| 7 | 60 | |
| 8 | 55 | |
| 9 | 79 |
| Value | Count | Frequency (%) |
| 22 | 12 | 1.2% |
| 21 | 24 | 2.4% |
| 20 | 22 | 2.2% |
| 19 | 34 | |
| 18 | 36 | |
| 17 | 59 | |
| 16 | 49 | |
| 15 | 58 | |
| 14 | 54 | |
| 13 | 74 |
| Distinct | 1000 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 7.9 KiB |
| 0721 Meadow Ridge Pass | 1 |
|---|---|
| 3 Golden Leaf Point | 1 |
| 68 Anthes Park | 1 |
| 15 Weeping Birch Crossing | 1 |
| 45 Becker Place | 1 |
| Other values (995) |
Length
| Max length | 26 |
|---|---|
| Median length | 18 |
| Mean length | 17.582 |
| Min length | 9 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 1000 ? |
|---|---|
| Unique (%) | 100.0% |
Sample
| 1st row | 45 Shopko Center |
|---|---|
| 2nd row | 14 Mccormick Park |
| 3rd row | 5 Colorado Crossing |
| 4th row | 207 Annamark Plaza |
| 5th row | 115 Montana Place |
Common Values
| Value | Count | Frequency (%) |
| 0721 Meadow Ridge Pass | 1 | 0.1% |
| 3 Golden Leaf Point | 1 | 0.1% |
| 68 Anthes Park | 1 | 0.1% |
| 15 Weeping Birch Crossing | 1 | 0.1% |
| 45 Becker Place | 1 | 0.1% |
| 1969 Melody Lane | 1 | 0.1% |
| 2886 Buena Vista Terrace | 1 | 0.1% |
| 7870 Stuart Crossing | 1 | 0.1% |
| 2382 Anthes Crossing | 1 | 0.1% |
| 51 Hooker Court | 1 | 0.1% |
| Other values (990) | 990 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| crossing | 59 | 1.9% |
| park | 59 | 1.9% |
| center | 58 | 1.9% |
| lane | 55 | 1.8% |
| street | 55 | 1.8% |
| avenue | 55 | 1.8% |
| point | 54 | 1.7% |
| hill | 51 | 1.6% |
| plaza | 50 | 1.6% |
| court | 49 | 1.6% |
| Other values (1137) | 2563 |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
| Distinct | 522 |
|---|---|
| Distinct (%) | 52.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 3019.227 |
| Minimum | 2000 |
|---|---|
| Maximum | 4879 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 7.9 KiB |
Quantile statistics
| Minimum | 2000 |
|---|---|
| 5-th percentile | 2046 |
| Q1 | 2209 |
| median | 2800 |
| Q3 | 3845.5 |
| 95-th percentile | 4508.05 |
| Maximum | 4879 |
| Range | 2879 |
| Interquartile range (IQR) | 1636.5 |
Descriptive statistics
| Standard deviation | 848.8957672 |
|---|---|
| Coefficient of variation (CV) | 0.2811632803 |
| Kurtosis | -1.142498217 |
| Mean | 3019.227 |
| Median Absolute Deviation (MAD) | 635.5 |
| Skewness | 0.4921079268 |
| Sum | 3019227 |
| Variance | 720624.0235 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 2145 | 9 | 0.9% |
| 2232 | 9 | 0.9% |
| 2148 | 7 | 0.7% |
| 3029 | 7 | 0.7% |
| 3977 | 7 | 0.7% |
| 4207 | 7 | 0.7% |
| 2750 | 7 | 0.7% |
| 2168 | 7 | 0.7% |
| 2026 | 6 | 0.6% |
| 2560 | 6 | 0.6% |
| Other values (512) | 928 |
| Value | Count | Frequency (%) |
| 2000 | 1 | 0.1% |
| 2007 | 3 | |
| 2009 | 2 | |
| 2010 | 4 | |
| 2011 | 4 | |
| 2015 | 1 | 0.1% |
| 2016 | 2 | |
| 2017 | 1 | 0.1% |
| 2019 | 3 | |
| 2022 | 1 | 0.1% |
| Value | Count | Frequency (%) |
| 4879 | 1 | 0.1% |
| 4852 | 1 | 0.1% |
| 4818 | 2 | |
| 4817 | 2 | |
| 4814 | 3 | |
| 4744 | 1 | 0.1% |
| 4740 | 2 | |
| 4720 | 1 | 0.1% |
| 4717 | 1 | 0.1% |
| 4710 | 1 | 0.1% |
| Distinct | 3 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 7.9 KiB |
| NSW | |
|---|---|
| VIC | |
| QLD |
Length
| Max length | 3 |
|---|---|
| Median length | 3 |
| Mean length | 3 |
| Min length | 3 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | QLD |
|---|---|
| 2nd row | NSW |
| 3rd row | VIC |
| 4th row | QLD |
| 5th row | NSW |
Common Values
| Value | Count | Frequency (%) |
| NSW | 506 | |
| VIC | 266 | |
| QLD | 228 |
Length
Histogram of lengths of the category
Pie chart
| Value | Count | Frequency (%) |
| nsw | 506 | |
| vic | 266 | |
| qld | 228 |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
| Distinct | 1 |
|---|---|
| Distinct (%) | 0.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 7.9 KiB |
| Australia |
|---|
Length
| Max length | 9 |
|---|---|
| Median length | 9 |
| Mean length | 9 |
| Min length | 9 |
Characters and Unicode
| Total characters | 0 |
|---|---|
| Distinct characters | 0 |
| Distinct categories | 0 ? |
| Distinct scripts | 0 ? |
| Distinct blocks | 0 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | Australia |
|---|---|
| 2nd row | Australia |
| 3rd row | Australia |
| 4th row | Australia |
| 5th row | Australia |
Common Values
| Value | Count | Frequency (%) |
| Australia | 1000 |
Length
Histogram of lengths of the category
Pie chart
| Value | Count | Frequency (%) |
| australia | 1000 |
Most occurring characters
| Value | Count | Frequency (%) |
| No values found. | ||
Most occurring categories
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per category
Most occurring scripts
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per script
Most occurring blocks
| Value | Count | Frequency (%) |
| No values found. | ||
Most frequent character per block
| Distinct | 12 |
|---|---|
| Distinct (%) | 1.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 7.397 |
| Minimum | 1 |
|---|---|
| Maximum | 12 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 7.9 KiB |
Quantile statistics
| Minimum | 1 |
|---|---|
| 5-th percentile | 2 |
| Q1 | 6 |
| median | 8 |
| Q3 | 9 |
| 95-th percentile | 11 |
| Maximum | 12 |
| Range | 11 |
| Interquartile range (IQR) | 3 |
Descriptive statistics
| Standard deviation | 2.758804452 |
|---|---|
| Coefficient of variation (CV) | 0.3729626134 |
| Kurtosis | -0.3712799928 |
| Mean | 7.397 |
| Median Absolute Deviation (MAD) | 2 |
| Skewness | -0.5576112079 |
| Sum | 7397 |
| Variance | 7.611002002 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=12)
| Value | Count | Frequency (%) |
| 9 | 173 | |
| 8 | 162 | |
| 7 | 138 | |
| 10 | 116 | |
| 6 | 70 | |
| 11 | 62 | 6.2% |
| 5 | 57 | 5.7% |
| 4 | 53 | 5.3% |
| 3 | 51 | 5.1% |
| 12 | 46 | 4.6% |
| Other values (2) | 72 |
| Value | Count | Frequency (%) |
| 1 | 30 | 3.0% |
| 2 | 42 | 4.2% |
| 3 | 51 | 5.1% |
| 4 | 53 | 5.3% |
| 5 | 57 | 5.7% |
| 6 | 70 | |
| 7 | 138 | |
| 8 | 162 | |
| 9 | 173 | |
| 10 | 116 |
| Value | Count | Frequency (%) |
| 12 | 46 | 4.6% |
| 11 | 62 | 6.2% |
| 10 | 116 | |
| 9 | 173 | |
| 8 | 162 | |
| 7 | 138 | |
| 6 | 70 | |
| 5 | 57 | 5.7% |
| 4 | 53 | 5.3% |
| 3 | 51 | 5.1% |
| Distinct | 324 |
|---|---|
| Distinct (%) | 32.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 498.819 |
| Minimum | 1 |
|---|---|
| Maximum | 1000 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 7.9 KiB |
Quantile statistics
| Minimum | 1 |
|---|---|
| 5-th percentile | 50 |
| Q1 | 250 |
| median | 500 |
| Q3 | 750.25 |
| 95-th percentile | 948.15 |
| Maximum | 1000 |
| Range | 999 |
| Interquartile range (IQR) | 500.25 |
Descriptive statistics
| Standard deviation | 288.8109971 |
|---|---|
| Coefficient of variation (CV) | 0.5789895675 |
| Kurtosis | -1.200749808 |
| Mean | 498.819 |
| Median Absolute Deviation (MAD) | 250 |
| Skewness | 0.001245859611 |
| Sum | 498819 |
| Variance | 83411.79203 |
| Monotonicity | Increasing |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 760 | 13 | 1.3% |
| 259 | 12 | 1.2% |
| 455 | 9 | 0.9% |
| 133 | 9 | 0.9% |
| 386 | 9 | 0.9% |
| 904 | 9 | 0.9% |
| 700 | 8 | 0.8% |
| 312 | 8 | 0.8% |
| 820 | 8 | 0.8% |
| 536 | 8 | 0.8% |
| Other values (314) | 907 |
| Value | Count | Frequency (%) |
| 1 | 3 | |
| 4 | 2 | |
| 6 | 2 | |
| 8 | 2 | |
| 10 | 2 | |
| 12 | 1 | 0.1% |
| 13 | 1 | 0.1% |
| 14 | 2 | |
| 16 | 1 | 0.1% |
| 17 | 2 |
| Value | Count | Frequency (%) |
| 1000 | 1 | 0.1% |
| 997 | 3 | |
| 996 | 1 | 0.1% |
| 994 | 2 | 0.2% |
| 993 | 1 | 0.1% |
| 988 | 5 | |
| 987 | 1 | 0.1% |
| 985 | 2 | 0.2% |
| 983 | 2 | 0.2% |
| 979 | 4 |
| Distinct | 324 |
|---|---|
| Distinct (%) | 32.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 0.8817140938 |
| Minimum | 0.34 |
|---|---|
| Maximum | 1.71875 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 7.9 KiB |
Quantile statistics
| Minimum | 0.34 |
|---|---|
| 5-th percentile | 0.45655625 |
| Q1 | 0.64953125 |
| median | 0.86 |
| Q3 | 1.075 |
| 95-th percentile | 1.40625 |
| Maximum | 1.71875 |
| Range | 1.37875 |
| Interquartile range (IQR) | 0.42546875 |
Descriptive statistics
| Standard deviation | 0.293524508 |
|---|---|
| Coefficient of variation (CV) | 0.3329021392 |
| Kurtosis | -0.4524719248 |
| Mean | 0.8817140938 |
| Median Absolute Deviation (MAD) | 0.213125 |
| Skewness | 0.4299025249 |
| Sum | 881.7140938 |
| Variance | 0.08615663677 |
| Monotonicity | Decreasing |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 0.6375 | 13 | 1.3% |
| 1.0625 | 12 | 1.2% |
| 0.945625 | 9 | 0.9% |
| 0.8925 | 9 | 0.9% |
| 0.5 | 9 | 0.9% |
| 1.2375 | 9 | 0.9% |
| 1.02 | 8 | 0.8% |
| 0.6875 | 8 | 0.8% |
| 0.584375 | 8 | 0.8% |
| 0.825 | 8 | 0.8% |
| Other values (314) | 907 |
| Value | Count | Frequency (%) |
| 0.34 | 1 | 0.1% |
| 0.357 | 3 | |
| 0.374 | 1 | 0.1% |
| 0.3825 | 2 | 0.2% |
| 0.391 | 1 | 0.1% |
| 0.3995 | 5 | |
| 0.4 | 1 | 0.1% |
| 0.408 | 2 | 0.2% |
| 0.41 | 2 | 0.2% |
| 0.4165 | 4 |
| Value | Count | Frequency (%) |
| 1.71875 | 3 | |
| 1.703125 | 2 | |
| 1.671875 | 2 | |
| 1.65625 | 2 | |
| 1.640625 | 2 | |
| 1.625 | 1 | 0.1% |
| 1.609375 | 1 | 0.1% |
| 1.59375 | 2 | |
| 1.5625 | 1 | 0.1% |
| 1.546875 | 2 |
Spearman's ρ
The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
Pearson's r
The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
Kendall's τ
Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
Cramér's V (φc)
Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.Phik (φk)
Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here. A simple visualization of nullity by column.
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.
First rows
| first_name | last_name | gender | past_3_years_bike_related_purchases | DOB | job_title | job_industry_category | wealth_segment | deceased_indicator | owns_car | tenure | address | postcode | state | country | property_valuation | Rank | Value | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Chickie | Brister | Male | 86 | 1957-07-12 | General Manager | Manufacturing | Mass Customer | N | Yes | 14 | 45 Shopko Center | 4500 | QLD | Australia | 6 | 1 | 1.718750 |
| 1 | Morly | Genery | Male | 69 | 1970-03-22 | Structural Engineer | Property | Mass Customer | N | No | 16 | 14 Mccormick Park | 2113 | NSW | Australia | 11 | 1 | 1.718750 |
| 2 | Ardelis | Forrester | Female | 10 | 1974-08-28 | Senior Cost Accountant | Financial Services | Affluent Customer | N | No | 10 | 5 Colorado Crossing | 3505 | VIC | Australia | 5 | 1 | 1.718750 |
| 3 | Lucine | Stutt | Female | 64 | 1979-01-28 | Account Representative III | Manufacturing | Affluent Customer | N | Yes | 5 | 207 Annamark Plaza | 4814 | QLD | Australia | 1 | 4 | 1.703125 |
| 4 | Melinda | Hadlee | Female | 34 | 1965-09-21 | Financial Analyst | Financial Services | Affluent Customer | N | No | 19 | 115 Montana Place | 2093 | NSW | Australia | 9 | 4 | 1.703125 |
| 5 | Druci | Brandli | Female | 39 | 1951-04-29 | Assistant Media Planner | Entertainment | High Net Worth | N | Yes | 22 | 89105 Pearson Terrace | 4075 | QLD | Australia | 7 | 6 | 1.671875 |
| 6 | Rutledge | Hallt | Male | 23 | 1976-10-06 | Compensation Analyst | Financial Services | Mass Customer | N | No | 8 | 7 Nevada Crossing | 2620 | NSW | Australia | 7 | 6 | 1.671875 |
| 7 | Nancie | Vian | Female | 74 | 1972-12-27 | Human Resources Assistant II | Retail | Mass Customer | N | Yes | 10 | 85 Carioca Point | 4814 | QLD | Australia | 5 | 8 | 1.656250 |
| 8 | Duff | Karlowicz | Male | 50 | 1972-04-28 | Speech Pathologist | Manufacturing | Mass Customer | N | Yes | 5 | 717 West Drive | 2200 | NSW | Australia | 10 | 8 | 1.656250 |
| 9 | Barthel | Docket | Male | 72 | 1985-08-02 | Accounting Assistant IV | IT | Mass Customer | N | Yes | 17 | 80 Scofield Junction | 4151 | QLD | Australia | 5 | 10 | 1.640625 |
Last rows
| first_name | last_name | gender | past_3_years_bike_related_purchases | DOB | job_title | job_industry_category | wealth_segment | deceased_indicator | owns_car | tenure | address | postcode | state | country | property_valuation | Rank | Value | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 990 | Jermaine | Bagshawe | Female | 60 | 1954-05-14 | Help Desk Operator | Property | Mass Customer | N | Yes | 9 | 260 Briar Crest Drive | 4209 | QLD | Australia | 6 | 988 | 0.3995 |
| 991 | Bryan | Jachtym | Male | 59 | 1974-05-15 | Automation Specialist I | Manufacturing | Mass Customer | N | Yes | 15 | 56 Moland Crossing | 3356 | VIC | Australia | 3 | 988 | 0.3995 |
| 992 | Renie | Laundon | Female | 32 | 1973-12-18 | Assistant Media Planner | Entertainment | Mass Customer | N | Yes | 8 | 1 Shelley Pass | 4118 | QLD | Australia | 3 | 993 | 0.3910 |
| 993 | Weidar | Etheridge | Male | 38 | 1959-07-13 | Compensation Analyst | Financial Services | Mass Customer | N | Yes | 6 | 0535 Jay Point | 2422 | NSW | Australia | 4 | 994 | 0.3825 |
| 994 | Datha | Fishburn | Female | 15 | 1990-07-02 | Office Assistant IV | Retail | Mass Customer | N | No | 3 | 6 Caliangt Way | 3079 | VIC | Australia | 12 | 994 | 0.3825 |
| 995 | Ferdinand | Romanetti | Male | 60 | 1959-10-07 | Paralegal | Financial Services | Affluent Customer | N | No | 9 | 2 Sloan Way | 2200 | NSW | Australia | 7 | 996 | 0.3740 |
| 996 | Burk | Wortley | Male | 22 | 2001-10-17 | Senior Sales Associate | Health | Mass Customer | N | No | 6 | 04 Union Crossing | 2196 | NSW | Australia | 10 | 997 | 0.3570 |
| 997 | Melloney | Temby | Female | 17 | 1954-10-05 | Budget/Accounting Analyst IV | Financial Services | Affluent Customer | N | Yes | 15 | 33475 Fair Oaks Junction | 4702 | QLD | Australia | 2 | 997 | 0.3570 |
| 998 | Dickie | Cubbini | Male | 30 | 1952-12-17 | Financial Advisor | Financial Services | Mass Customer | N | Yes | 19 | 57666 Victoria Way | 4215 | QLD | Australia | 2 | 997 | 0.3570 |
| 999 | Sylas | Duffill | Male | 56 | 1955-10-02 | Staff Accountant IV | Property | Mass Customer | N | Yes | 14 | 21875 Grover Drive | 2010 | NSW | Australia | 9 | 1000 | 0.3400 |